Band 3, Heft 3

Autonomy and flexibility are two major requirements for modern robots. In particular, humanoid robots should learn new skills incrementally through autonomous exploration, and adapt to different contexts. In this paper we consider the problem of learning forward models for task space control under dynamically varying kinematic contexts: the robot learns incrementally and autonomously its forward kinematics under different contexts, represented by the inclusion of different tools, and exploits the learned model to realize reaching with those tools. We model the forward kinematics as a multi-valued function, in which different outputs for the same input query are related to different tools (i.e. contexts). The model is estimated using IMLE, a recent online learning algorithm for multi-valued regression, and used for control. No information is given about the tool changes, nor any assumption is made about the tool kinematics. Results are provided both in simulation and with a full-body humanoid. In the latter case we show how the robot successfully performs reaching using a flexible tool, a clear example of complex kinematics.

Gaze control requires the coordination of movements of both eyes and head to fixate on a target. Using our biologically constrained architecture for gaze control we show how the relationships between the coupled sensorimotor systems can be learnt autonomously from scratch, allowing for adaptation as the system grows or changes. Infant studies suggest developmental learning strategies, which can be applied to sensorimotor learning in humanoid robots. We examine environmental constraints for the learning of eye and head coupled mappings, and give results from implementations on an iCub robot. The results show the impact of these constraints and how they can be overcome to benefit the development of fast, cumulative, on-line learning of coupled sensorimotor systems.

The “Policy Improvement with Path Integrals” (PI 2 ) [25] and “Covariance Matrix Adaptation - Evolutionary Strategy” [8] are considered to be state-of-the-art in direct reinforcement learning and stochastic optimization respectively. We have recently shown that incorporating covariance matrix adaptation into PI 2 – which yields the PI CMA 2 algorithm – enables adaptive exploration by continually and autonomously reconsidering the exploration/exploitation trade-off. In this article, we provide an overview of our recent work on covariance matrix adaptation for direct reinforcement learning [22–24], highlight its relevance to developmental robotics, and conduct further experiments to analyze the results. We investigate two complementary phenomena from developmental robotics. First, we demonstrate PI CMA 2 ’s ability to adapt to slowly or abruptly changing tasks due to its continual and adaptive exploration. This is an important component of life-long skill learning in dynamic environments. Second, we show on a reaching task PI CMA 2 how subsequently releases degrees of freedom from proximal to more distal limbs as learning progresses. A similar effect is observed in human development, where it is known as ‘proximodistal maturation’.

We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal-babbling. It learns at the same time when, who and what to actively imitate from several available teachers, and learns when not to use social guidance but use active goal-oriented self-exploration. This is formalised in the framework of life-long strategic learning. The proposed architecture, called Socially Guided Intrinsic Motivation with Active Choice of Teacher and Strategy (SGIM-ACTS), relies on hierarchical active decisions of what and how to learn driven by empirical evaluation of learning progress for each learning strategy. We illustrate with an experiment where a simulated robot learns to control its arm for realising two kinds of different outcomes. It has to choose actively and hierarchically at each learning episode: 1) what to learn: which outcome is most interesting to select as a goal to focus on for goal-directed exploration; 2) how to learn: which data collection strategy to use among self-exploration, mimicry and emulation; 3) once he has decided when and what to imitate by choosing mimicry or emulation, then he has to choose who to imitate, from a set of different teachers. We show that SGIM-ACTS learns significantly more efficiently than using single learning strategies, and coherently selects the best strategy with respect to the chosen outcome, taking advantage of the available teachers (with different levels of skills).

Future robots must co-exist and directly interact with human beings. Designing these agents imply solving hard problems linked to human-robot interaction tasks. For instance, how a robot can choose an interacting partner among various agents and how a robot locates regions of interest in its visual field. Studies of neurobiology and psychology collectively named synchrony as an indispensable parameter for social interaction. We assumed that Human-Robot interaction could be initiated by synchrony detection. In this paper, we present a developmental approach for analyzing unintentional synchronization in human-robot interaction. Using our neural network model, the robot learns from a babbling step its inner dynamics by associating its own motor activities (oscillators) with the visual stimulus induced by its own motion. After learning the robot is capable of choosing an interacting agent and of localizing the spatial position of its preferred partner by synchrony detection.